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# SPDX-License-Identifier: Apache-2.0
"""Prefill postprocessing and device-sampling behavior."""
from __future__ import annotations
from typing import Any, Callable, Iterable
import torch
import ttnn
from models.common.llm_runtime.prefill.config import PrefillRuntimeConfig
from models.common.llm_runtime.prefill.inputs import PrefillPositionInputs
from models.common.llm_runtime.prefill.plan import PrefillRequest
from models.common.llm_runtime.prefill.sampling_helpers import _TILE_SIZE, SamplingPath
from models.common.llm_runtime.prefill.signatures import PreparedPrefill
from models.common.modules.sampling.params import PreparedSamplingParams, prepare_sampling_params
from models.common.sampling.sampling_params import SamplingParams
KPTSignature = tuple[tuple[int, ...], tuple[float, ...], tuple[float, ...]] | None
class PrefillPostprocessor:
"""Own prefill output selection, sampling tensors, and finalization."""
def __init__(
self,
config: PrefillRuntimeConfig,
*,
allocate_device_tensors: Callable[[Any], Any],
copy_into_device_tensors: Callable[[Any, Any], Any],
) -> None:
self.config = config
self._allocate_device_tensors = allocate_device_tensors
self._copy_into_device_tensors = copy_into_device_tensors
def configure(self, config: PrefillRuntimeConfig) -> None:
self.config = config
def prepared_sampling(self, prepared: Any) -> PreparedSamplingParams | None:
"""Return native prepared state, adapting older test collaborators."""
value = getattr(prepared, "prepared_sampling", None)
if value is not None:
return value
raw = getattr(prepared, "sampling_params", None)
if raw is None:
return None
return prepare_sampling_params(
raw,
self.sampling_output_rows(prepared),
max_device_top_k=self.config.max_device_top_k,
allow_force_argmax=self.config.allow_force_argmax,
)
def validate_sampling_request(self, sampling_params: SamplingParams | None) -> None:
if sampling_params is not None and not self.config.device_sampling_enabled:
raise ValueError("sampling parameters were supplied while device sampling is disabled")
def classify_sampling_path(
self,
request: PrefillRequest,
sampling_params: PreparedSamplingParams | None,
) -> SamplingPath:
if sampling_params is None:
return "logits"
if request.kind != "single":
return "topk"
return sampling_params.sampling_path
def sampling_batch_size(self, request: PrefillRequest) -> int:
if self.config.device_sampling_enabled:
return self.config.sampling_batch_size
return request.padded_batch_size
def sampling_output_rows(self, prepared: PreparedPrefill) -> int:
# TT sampling validates K/P/T against the physical logits row count.
# The static Q128 path retains one complete tile and selects the exact
# logical row on the host, so its sampling tensors must span that tile.
if self.uses_static_q128_topk(prepared.request, prepared.sampling_path):
return _TILE_SIZE
return self.sampling_batch_size(prepared.request)
def uses_static_q128_topk(
self,
request: PrefillRequest,
sampling_path: SamplingPath,
) -> bool:
return (
self.config.static_q128_topk_supported
and sampling_path == "topk"
and request.kind == "single"
and not request.uses_chunked_prefill
and request.padded_sequence_length == 128
)
def make_device_kpt(
self,
sampling_params: PreparedSamplingParams | None,
batch_size: int,
force_topk: bool,
) -> tuple[Any, Any, Any] | None:
host = self.make_host_kpt(sampling_params, batch_size, force_topk)
if host is None:
return None
return tuple(self._allocate_device_tensors(host))
def make_host_kpt(
self,
sampling_params: PreparedSamplingParams | None,
batch_size: int,
force_topk: bool,
) -> tuple[Any, Any, Any] | None:
if sampling_params is None:
return None
if not force_topk and sampling_params.sampling_path == "argmax":
return None
if batch_size > sampling_params.batch_size:
raise ValueError("prepared sampling parameters do not cover the physical prefill batch")
k, p, temperature = self.kpt_values(sampling_params, batch_size)
mapper = ttnn.ReplicateTensorToMesh(self.config.mesh_device)
return (
ttnn.from_torch(
torch.tensor(k, dtype=torch.int32),
device=None,
dtype=ttnn.uint32,
layout=ttnn.ROW_MAJOR_LAYOUT,
mesh_mapper=mapper,
),
ttnn.from_torch(
torch.tensor(p, dtype=torch.float32),
device=None,
dtype=ttnn.bfloat16,
layout=ttnn.ROW_MAJOR_LAYOUT,
mesh_mapper=mapper,
),
ttnn.from_torch(
torch.tensor(temperature, dtype=torch.float32),
device=None,
dtype=ttnn.bfloat16,
layout=ttnn.ROW_MAJOR_LAYOUT,
mesh_mapper=mapper,
),
)
@staticmethod
def kpt_values(sampling_params: PreparedSamplingParams, batch_size: int):
active_rows = tuple(row for row, active in enumerate(sampling_params.active_mask) if active)
if len(active_rows) == 1 and batch_size > 1:
row = active_rows[0]
return (
(sampling_params.top_k[row],) * batch_size,
(sampling_params.top_p[row],) * batch_size,
(sampling_params.temperature[row],) * batch_size,
)
return (
sampling_params.top_k[:batch_size],
sampling_params.top_p[:batch_size],
sampling_params.temperature[:batch_size],
)
def refresh_kpt(
self,
device_kpt: tuple[Any, Any, Any] | None,
sampling_params: PreparedSamplingParams | None,
batch_size: int,
force_topk: bool,
) -> None:
host_kpt = self.make_host_kpt(sampling_params, batch_size, force_topk)
if (host_kpt is None) != (device_kpt is None):
raise RuntimeError("sampling parameters changed the compiled sampling path")
if host_kpt is not None:
self._copy_into_device_tensors(host_kpt, device_kpt)
def refresh_workspace_sampling(
self,
prepared: PreparedPrefill,
*,
kpt: tuple[Any, Any, Any] | None,
kpt_signature: KPTSignature,
) -> KPTSignature:
if prepared.sampling_path != "topk":
return kpt_signature
sampling_batch_size = self.sampling_output_rows(prepared)
prepared_sampling = self.prepared_sampling(prepared)
if prepared_sampling is None:
kpt_value = None
else:
sampling = prepared_sampling
kpt_value = self.kpt_values(sampling, sampling_batch_size)
if kpt_signature != kpt_value:
self.refresh_kpt(
kpt,
prepared_sampling,
sampling_batch_size,
force_topk=True,
)
return kpt_value
return kpt_signature
def make_sampling_output(self, batch_size: int) -> Any:
return ttnn.from_torch(
torch.zeros((1, 1, 1, int(batch_size)), dtype=torch.int32),
device=self.config.mesh_device,
dtype=ttnn.uint32,
layout=ttnn.ROW_MAJOR_LAYOUT,
memory_config=ttnn.DRAM_MEMORY_CONFIG,
mesh_mapper=ttnn.ReplicateTensorToMesh(self.config.mesh_device),
)
def sample_device(
self,
logits: Any,
kpt: tuple[Any, Any, Any] | None,
sampling: PreparedSamplingParams,
sampled_output: Any | None = None,
*,
count_tokens: bool = True,
) -> Any:
controller = self.config.sampling_state_controller
if controller is not None:
return controller.prefill_forward(
logits,
self.config.sampling_state,
sampling,
k=None if kpt is None else kpt[0],
p=None if kpt is None else kpt[1],
temp=None if kpt is None else kpt[2],
tt_out_tok=sampled_output,
count_tokens=count_tokens,
)
if kpt is None:
return self.config.model.sampling.decode_forward(
logits,
tt_out_tok=sampled_output,
enable_log_probs=sampling.enable_log_probs,
)
return self.config.model.sampling.decode_forward(
logits,
k=kpt[0],
p=kpt[1],
temp=kpt[2],
tt_out_tok=sampled_output,
enable_log_probs=sampling.enable_log_probs,
)
def finish_regular_prefill(
self,
prepared: PreparedPrefill,
hidden: Any,
kpt: tuple[Any, Any, Any] | None,
position_inputs: PrefillPositionInputs,
*,
sampled_output: Any | None = None,
owned: list[Any] | None = None,
count_tokens: bool = True,
) -> Any:
request = prepared.request
relative_last = [last - cached for last, cached in zip(request.last_token_indices, request.cached_tokens)]
if request.kind == "batched" and not self.config.batched_prefill_batched_extract:
hidden = ttnn.reshape(
hidden,
[request.padded_batch_size, 1, request.padded_sequence_length, int(hidden.shape[-1])],
)
outputs = []
for local_row, last_token in enumerate(relative_last):
logits = self.config.model.post_process_prefill_output(
hidden[local_row : local_row + 1],
last_token,
)
retain_owned(owned, logits)
output = ttnn.untilize(logits, use_multicore=True)
retain_owned(owned, output)
outputs.append(output)
return outputs
if request.kind == "batched":
padded_last = list(relative_last) + [0] * (request.padded_batch_size - len(relative_last))
logits = self.config.model.post_process_batched_prefill_output(
hidden,
padded_last,
request.padded_batch_size,
request.padded_sequence_length,
)
elif self.uses_static_q128_topk(request, prepared.sampling_path):
logits = self.config.model.post_process_prefill_output(hidden, relative_last[0])
else:
# Runtime last-token bounds and row index for host sampling too. The static path keyed
# both its 32-row tile slice and the final 1-row slice on the prompt offset, so every
# new offset compiled fresh programs on the first real request - after warmup had
# recorded the decode traces (measured: 2 buffers left live across replays per model).
# With the device-side row pick the logits already hold the last token in row 0.
logits = self.config.model.post_process_prefill_output(
hidden,
relative_last[0],
last_token_slice=(position_inputs.slice_start, position_inputs.slice_end),
last_token_index=position_inputs.row_index,
)
retain_owned(owned, logits)
if prepared.sampling_params is not None:
selected = fit_prefill_sampling_logits(logits, self.sampling_output_rows(prepared))
retain_owned(owned, selected)
output = self.sample_device(
selected,
kpt,
self.prepared_sampling(prepared),
sampled_output,
count_tokens=count_tokens,
)
else:
output = ttnn.untilize(logits, use_multicore=True)
if request.kind == "single" and not request.uses_chunked_prefill and int(output.shape[2]) > 1:
# Host sampling uses runtime row selection above. If the model returns a
# padded tile, the selected token is in row 0; discard the extra rows.
retain_owned(owned, output)
output = ttnn.slice(output, (0, 0, 0, 0), (1, 1, 1, int(output.shape[-1])))
retain_owned(owned, output)
return output
def finish_prefill_sequence(
self,
prepared: PreparedPrefill,
final_step_output: Any,
kpt: tuple[Any, Any, Any] | None,
position_inputs: PrefillPositionInputs,
*,
sampled_output: Any | None,
owned: list[Any],
count_tokens: bool = True,
) -> Any:
if not prepared.request.uses_chunked_prefill:
return self.finish_regular_prefill(
prepared,
final_step_output,
kpt,
position_inputs,
sampled_output=sampled_output,
owned=owned,
count_tokens=count_tokens,
)
if prepared.sampling_params is not None:
selected = fit_prefill_sampling_logits(final_step_output, self.sampling_output_rows(prepared))
retain_owned(owned, selected)
output = self.sample_device(
selected,
kpt,
self.prepared_sampling(prepared),
sampled_output,
count_tokens=count_tokens,
)
else:
output = ttnn.untilize(final_step_output, use_multicore=True)
retain_owned(owned, output)
return output
def retain_owned(owned: list[Any] | None, value: Any) -> None:
if owned is None or value is None or any(existing is value for existing in owned):
return
owned.append(value)
def without_borrowed(values: Iterable[Any], borrowed: Iterable[Any]) -> tuple[Any, ...]:
"""Remove trace/workspace-owned leaves from replay-local ownership."""
borrowed_ids = {id(value) for value in borrowed if value is not None}
def prune(value):
if value is None or id(value) in borrowed_ids:
return None
if isinstance(value, tuple):
kept = tuple(item for item in (prune(item) for item in value) if item is not None)
return kept or None
if isinstance(value, list):
kept = [item for item in (prune(item) for item in value) if item is not None]
return kept or None
return value
return tuple(item for item in (prune(value) for value in values) if item is not None)
def new_logprob_output(output: Any, persistent_sampled_output: Any | None) -> Any | None:
if persistent_sampled_output is None:
return None
if not isinstance(output, tuple) or len(output) != 2:
raise TypeError("sampled prefill output must contain (tokens, log_probs)")
return output[1]
def fit_prefill_sampling_logits(logits, target_batch: int):
target_batch = int(target_batch)
if target_batch <= 0:
raise ValueError("prefill sampling target batch must be positive")
current_batch = int(logits.shape[2])
if current_batch == target_batch:
return logits
if current_batch > target_batch:
return ttnn.slice(
logits,
(0, 0, 0, 0),
(logits.shape[0], logits.shape[1], target_batch, logits.shape[3]),
)
return ttnn.pad(logits, [(0, 0), (0, 0), (0, target_batch - current_batch), (0, 0)], value=0.0)
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